{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zECN53X4Wf7M"
   },
   "source": [
    "**Quick Start for RAG with `llmware`**\n",
    "\n",
    "This example illustrates a simple contract analysis using a small RAG-optimized LLM running locally\n",
    "\n",
    "*Note: Colab's built-in Python 3.10 comes with a newer version of `grcpio` (1.60) which is incompatible with a dependency of llmware. Downgrading `grcpio` requires a restart afterwards.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nGD_UZh4IfYm",
    "outputId": "3f74b4f8-1457-4559-faf1-7cf903abbe9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.10.12\n"
     ]
    }
   ],
   "source": [
    "!python -V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "971vCYB51IqI"
   },
   "outputs": [],
   "source": [
    "%pip install \"grpcio<=1.60.0,>=1.49.1\" --no-cache-dir ## just to be safe with enviroments\n",
    "!pip install -q llmware\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SKBTfwyTDr6F"
   },
   "source": [
    "🛑 In Colab click Runtime > Restart session and run all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RoFBsyUgyJni",
    "outputId": "956f1d28-5160-45aa-e53a-00ebe8a60c75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m22.1/22.1 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.4/300.4 kB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.8/143.8 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Building wheel for ai21 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for word2number (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "llmx 0.0.15a0 requires tiktoken, which is not installed.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -q llmware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "f34b375b7e464941b06fa55056186b1c",
      "0878975b308f410a9cd80c1f150f8edc",
      "353c350eec234922bcce1fd87b641be9",
      "c8238e040b8e4ebfade1b1f84bf3b9d4",
      "a1bfe7fb8dcf40f6956aadf34d879711",
      "881d89a137ab4a54b629cbaed4a5d116",
      "fc4f680743e149e7a329b3028d6aad10",
      "a93ea4fd7e314034b832f52766ffa741",
      "ad37d5cf8b714c038954998e094d8513",
      "b6e5202476c2459a96fd15f7c6e90173",
      "49620f0b97394ef6ab921b15beec8069",
      "fe76443ff5ae41268a310dea485a30a4",
      "ccb677fac5bc48b8ba2c5da953052c93",
      "21d7dc2e713c4ccf8b426e2bf16012fe",
      "413fbbae136a4c24b7153ef3017325f1",
      "5cfb42aa18244f57bcb799c6d17f1347",
      "406d86667d234d9aaff0ca844f14e402",
      "4f3d19d7e8f24fdb8d39a7a14e302338",
      "8f21514be40747f887f161a21698173d",
      "c90c25225aac40078311d8ee63a27892",
      "a740788a38604222b5594b999b70f0c8",
      "f9f989043c0d4312a656bb1f5a15a1da",
      "056b709c53a14074a1b295ad66a7e0a0",
      "f512d6a27ce245c6918a2f05f1b87a9d",
      "8ae4150d5f784bd4a56a4a42fe588b48",
      "cc2aa420b4cd44f0988ea1f6ee73d479",
      "e0b44f199ee54c58a0bcdb119cec52ef",
      "7e8a32daccbf4d7ab23bc5d587faacfd",
      "35e2957dc115421bba4a68399a51c9c9",
      "9378f057e81148869421d38c6e3d73ce",
      "e4c7f46621c945a690c55ed47c34dc77",
      "cf0d2867b8304913a2fad9e5115bfde8",
      "c113e36bd5704df4b30922533cddc88f"
     ]
    },
    "id": "cqWRMrztyCfR",
    "outputId": "8a22a3a0-09e4-48b0-bd11-44f129abc73d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " > Loading the llmware sample files...\n",
      "\n",
      " > Loading model llmware/bling-1b-0.1...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f34b375b7e464941b06fa55056186b1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/2.27k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe76443ff5ae41268a310dea485a30a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/4.11G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "056b709c53a14074a1b295ad66a7e0a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analyzing contract:  1 Leto EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Leto Apollo and TestCo Software, Inc.\n",
      "base salary : $200,000\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  2 Nyx EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Nyx Pan and the Company\n",
      "base salary : $200,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  3 Amphitrite EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Amphitrite Ares and TestCo Software, Inc.\n",
      "base salary : $5,000,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  4 Metis EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Metis Hades and TestCo Software, Inc.\n",
      "base salary : $200,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  5 Eileithyia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Eileithyia Hades (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
      "base salary : $300,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  6 Persephone EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Persephone Zeus and TestCo Software, Inc.\n",
      "base salary : $200,000\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  7 Rhea EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Rhea Hecate and TestCo Software, Inc.\n",
      "base salary : $4350,000\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  8 Demeter EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Demeter Dionysus and TestCo Software, Inc.\n",
      "base salary : $300,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  9 Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Athena Zeus (Executive) and TestCo Software, Inc. (Company or Employer).\n",
      "base salary : $500,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  10 Apollo EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Aphrodite Apollo (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
      "base salary : $600,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  11 Artemis Poseidon EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Artemus Poseidon and TestCo Software, Inc.\n",
      "base salary : $400,000\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  12 Gaia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Gaia Eros and TestCo Software, Inc.\n",
      "base salary : $250,000\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  14 Bia EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Bia Hermes (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
      "base salary : $400,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  15 Aphrodite EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Aphrodite Apollo (Executive) and TestCo Software, Inc. (the Company or Employer).\n",
      "base salary : $600,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Analyzing contract:  16 Nike EXECUTIVE EMPLOYMENT AGREEMENT.pdf\n",
      "LLM Responses:\n",
      "executive employment agreement : Nike Cronus and TestCo Software, Inc.\n",
      "base salary : $200,000.\n",
      "governing law : State of Massachusetts\n",
      "\n",
      "Prompt state saved at:  /root/llmware_data/prompt_history/63d2a23f-8f23-465c-96e7-a5a911ac1f5b\n",
      "csv output saved at:   {'report_name': 'interaction_report_Sun Jan 14 04:59:54 2024.csv', 'report_fp': '/root/llmware_data/prompt_history/interaction_report_Sun Jan 14 04:59:54 2024.csv', 'results': 45}\n"
     ]
    }
   ],
   "source": [
    "# This example illustrates a simple contract analysis\n",
    "# using a small RAG-optimized LLM running locally\n",
    "\n",
    "import os\n",
    "import re\n",
    "from llmware.prompts import Prompt, HumanInTheLoop\n",
    "from llmware.setup import Setup\n",
    "from llmware.configs import LLMWareConfig\n",
    "\n",
    "def contract_analysis_on_laptop (model_name):\n",
    "\n",
    "    # Load the llmware sample files\n",
    "    print (f\"\\n > Loading the llmware sample files...\")\n",
    "    sample_files_path = Setup().load_sample_files()\n",
    "    contracts_path = os.path.join(sample_files_path,\"Agreements\")\n",
    "\n",
    "    # query list\n",
    "    query_list = {\"executive employment agreement\": \"What are the name of the two parties?\",\n",
    "                  \"base salary\": \"What is the executive's base salary?\",\n",
    "                  \"governing law\": \"What is the governing law?\"}\n",
    "\n",
    "    print (f\"\\n > Loading model {model_name}...\")\n",
    "\n",
    "    prompter = Prompt().load_model(model_name)\n",
    "\n",
    "    for i, contract in enumerate(os.listdir(contracts_path)):\n",
    "\n",
    "        #   excluding Mac file artifact\n",
    "        if contract != \".DS_Store\":\n",
    "\n",
    "            print(\"\\nAnalyzing contract: \", str(i+1), contract)\n",
    "\n",
    "            print(\"LLM Responses:\")\n",
    "            for key, value in query_list.items():\n",
    "\n",
    "                # contract is parsed, text-chunked, and then filtered by topic key\n",
    "                source = prompter.add_source_document(contracts_path, contract, query=key)\n",
    "\n",
    "                # calling the LLM with 'source' information from the contract automatically packaged into the prompt\n",
    "                responses = prompter.prompt_with_source(value, prompt_name=\"just_the_facts\", temperature=0.3)\n",
    "\n",
    "                for r, response in enumerate(responses):\n",
    "                    print(key, \":\", re.sub(\"[\\n]\",\" \", response[\"llm_response\"]).strip())\n",
    "\n",
    "                # We're done with this contract, clear the source from the prompt\n",
    "                prompter.clear_source_materials()\n",
    "\n",
    "    # Save jsonl report to jsonl to /prompt_history folder\n",
    "    print(\"\\nPrompt state saved at: \", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))\n",
    "    prompter.save_state()\n",
    "\n",
    "    # Save csv report that includes the model, response, prompt, and evidence for human-in-the-loop review\n",
    "    csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()\n",
    "    print(\"csv output saved at:  \", csv_output)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    # use local cpu model - smallest, fastest (use larger BLING models for higher accuracy)\n",
    "    model = \"llmware/bling-1b-0.1\"\n",
    "\n",
    "    contract_analysis_on_laptop(model)"
   ]
  }
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